Adaptive resource partitioning in a wireless communication network

ABSTRACT

Techniques for performing adaptive resource partitioning are described. In one design, a node computes local metrics for different possible actions related to resource partitioning to allocate available resources to a set of nodes that includes the node. Each possible action is associated with a set of resource usage profiles for the set of nodes. The node sends the computed local metrics to at least one neighbor node in the set of nodes. The node also receives local metrics for the possible actions from the neighbor node(s). The node determines overall metrics for the possible actions based on the computed local metrics and the received local metrics. The node then determines allocation of the available resources to the set of nodes based on the overall metrics. For example, the node may select the action with the best overall metric and may utilize the available resources based on a resource usage profile for the selected action.

The present application claims priority to provisional U.S. ApplicationSer. No. 61/161,646, entitled “UTILITY-BASED RESOURCE COORDINATION FORHETEROGENEOUS NETWORKS,” filed Mar. 19, 2009, assigned to the assigneehereof and incorporated herein by reference.

BACKGROUND

I. Field

The present disclosure relates generally to communication, and morespecifically to techniques for supporting wireless communication.

II. Background

Wireless communication networks are widely deployed to provide variouscommunication content such as voice, video, packet data, messaging,broadcast, etc. These wireless networks may be multiple-access networkscapable of supporting multiple users by sharing the available networkresources. Examples of such multiple-access networks include CodeDivision Multiple Access (CDMA) networks, Time Division Multiple Access(TDMA) networks, Frequency Division Multiple Access (FDMA) networks,Orthogonal FDMA (OFDMA) networks, and Single-Carrier FDMA (SC-FDMA)networks.

A wireless communication network may include a number of base stationsthat can support communication for a number of user equipments (UEs). AUE may communicate with a base station via the downlink and uplink. Thedownlink (or forward link) refers to the communication link from thebase station to the UE, and the uplink (or reverse link) refers to thecommunication link from the UE to the base station.

A base station may transmit data on the downlink to a UE and/or mayreceive data on the uplink from the UE. On the downlink, a transmissionfrom the base station may observe interference due to transmissions fromneighbor base stations. On the uplink, a transmission from the UE mayobserve interference due to transmissions from other UEs communicatingwith the neighbor base stations. For both the downlink and uplink, theinterference due to interfering base stations and interfering UEs maydegrade performance. It may be desirable to mitigate interference inorder to improve performance.

SUMMARY

Techniques for performing adaptive resource partitioning in a wirelessnetwork are described herein. Resource partitioning refers to a processto allocate available resources to nodes. A node may be a base station,a relay, or some other entity. For adaptive resource partitioning, theavailable resources may be dynamically allocated to nodes in a mannersuch that good performance can be achieved.

In one design, adaptive resource partitioning may be performed in adistributed manner by each node in a set of nodes. In one design, agiven node in the set of nodes may compute local metrics for a pluralityof possible actions related to resource partitioning to allocateavailable resources to the set of nodes. Each possible action may beassociated with a set of resource usage profiles for the set of nodes.Each resource usage profile may indicate allowed usage of the availableresources by a particular node, e.g., a list of allowed transmit powerspectral density (PSD) levels for the available resources. The node maysend the computed local metrics to at least one neighbor node in the setof nodes. The node may also receive local metrics for the plurality ofpossible actions from the at least one neighbor node. The node maydetermine overall metrics for the plurality of possible actions based onthe computed local metrics and the received local metrics. The node maythen determine allocation of the available resources to the set of nodesbased on the overall metrics for the plurality of possible actions. Inone design, the node may select one of the possible actions based on theoverall metrics for these possible actions, e.g., select the possibleaction with the best overall metric. The node may then utilize theavailable resources based on a resource usage profile associated withthe selected action and applicable for the node. For example, the nodemay schedule data transmission for at least one UE on the availableresources based on the resource usage profile for the node.

Various aspects and features of the disclosure are described in furtherdetail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a wireless communication network.

FIG. 2 shows exemplary active sets for UEs and neighbor sets for nodes.

FIG. 3 shows a process for performing adaptive resource partitioning.

FIG. 4 shows a wireless network with adaptive resource partitioning.

FIG. 5 shows a process for supporting communicating.

FIG. 6 shows an apparatus for supporting communicating.

FIG. 7 shows a process for performing adaptive resource partitioning

FIG. 8 shows a process for communicating by a UE.

FIG. 9 shows an apparatus for communicating by a UE.

FIG. 10 shows a block diagram of a base station and a UE.

DETAILED DESCRIPTION

The techniques described herein may be used for various wirelesscommunication networks such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA andother networks. The terms “network” and “system” are often usedinterchangeably. A CDMA network may implement a radio technology such asUniversal Terrestrial Radio Access (UTRA), cdma2000, etc. UTRA includesWideband CDMA (WCDMA) and other variants of CDMA. cdma2000 coversIS-2000, IS-95 and IS-856 standards. A TDMA network may implement aradio technology such as Global System for Mobile Communications (GSM).An OFDMA network may implement a radio technology such as Evolved UTRA(E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16(WiMAX), IEEE 802.20, Flash-OFDM®, etc. UTRA and E-UTRA are part ofUniversal Mobile Telecommunication System (UMTS). 3GPP Long TermEvolution (LTE) and LTE-Advanced (LTE-A) are new releases of UMTS thatuse E-UTRA, which employs OFDMA on the downlink and SC-FDMA on theuplink. UTRA, E-UTRA, UMTS, LTE, LTE-A and GSM are described indocuments from an organization named “3rd Generation PartnershipProject” (3GPP). cdma2000 and UMB are described in documents from anorganization named “3rd Generation Partnership Project 2” (3GPP2). Thetechniques described herein may be used for the wireless networks andradio technologies mentioned above as well as other wireless networksand radio technologies.

FIG. 1 shows a wireless communication network 100, which may include anumber of base stations 110 and other network entities. A base stationmay be an entity that communicates with UEs and may also be referred toas a node, a Node B, an evolved Node B (eNB), an access point, etc. Eachbase station may provide communication coverage for a particulargeographic area. In 3GPP, the term “cell” can refer to a coverage areaof a base station and/or a base station subsystem serving this coveragearea, depending on the context in which the term is used. In 3GPP2, theterm “sector” or “cell-sector” can refer to a coverage area of a basestation and/or a base station subsystem serving this coverage area. Forclarity, 3GPP concept of “cell” is used in the description herein.

A base station may provide communication coverage for a macro cell, apico cell, a femto cell, and/or other types of cell. A macro cell maycover a relatively large geographic area (e.g., several kilometers inradius) and may allow unrestricted access by UEs with servicesubscription. A pico cell may cover a relatively small geographic areaand may allow unrestricted access by UEs with service subscription. Afemto cell may cover a relatively small geographic area (e.g., a home)and may allow restricted access by UEs having association with the femtocell (e.g., UEs in a Closed Subscriber Group (CSG)). In the exampleshown in FIG. 1, wireless network 100 includes macro base stations 110 aand 110 b for macro cells, pico base stations 110 c and 110 e for picocells, and a femto/home base station 110 d for a femto cell.

Wireless network 100 may also include relays. A relay may be an entitythat receives a transmission of data from an upstream entity (e.g., abase station or a UE) and sends a transmission of the data to adownstream entity (e.g., a UE or a base station). A relay may also be aUE that relays transmissions for other UEs. A relay may also be referredto as a node, a station, a relay station, a relay base station, etc.

Wireless network 100 may be a heterogeneous network that includes basestations of different types, e.g., macro base stations, pico basestations, femto base stations, relays, etc. These different types ofbase stations may have different transmit power levels, differentcoverage areas, and different impact on interference in wireless network100. For example, macro base stations may have a high transmit powerlevel (e.g., 20 Watts or 43 dBm), pico base stations may have a lowertransmit power level (e.g., 2 Watts or 33 dBm), and femto base stationsmay have a low transmit power level (e.g., 0.2 Watts or 23 dBm).Different types of base stations may belong in different power classeshaving different maximum transmit power levels.

A network controller 130 may couple to a set of base stations and mayprovide coordination and control for these base stations. Networkcontroller 130 may communicate with base stations 110 via a backhaul.Base stations 110 may also communicate with one another via thebackhaul.

UEs 120 may be dispersed throughout wireless network 100, and each UEmay be stationary or mobile. A UE may also be referred to as a station,a terminal, a mobile station, a subscriber unit, etc. A UE may be acellular phone, a personal digital assistant (PDA), a wireless modem, awireless communication device, a handheld device, a laptop computer, acordless phone, a wireless local loop (WLL) station, etc. A UE may beable to communicate with base stations, relays, other UEs, etc.

A UE may be located within the coverage of one or more base stations. Inone design, a single base station may be selected to serve the UE onboth the downlink and uplink. In another design, one base station may beselected to serve the UE on each of the downlink and uplink. For bothdesigns, a serving base station may be selected based on one or morecriteria such as maximum geometry, minimum pathloss, maximumenergy/interference efficiency, maximum user throughput, etc. Geometryrelates to received signal quality, which may be quantified by acarrier-over-thermal (CoT), a signal-to-noise ratio (SNR), asignal-to-noise-and-interference ratio (SINR), a carrier-to-interferenceratio (C/I), etc. Maximizing energy/interference efficiency may entail(i) minimizing a required transmit energy per bit or (ii) minimizing areceived interference energy per unit of received useful signal energy.Part (ii) may correspond to maximizing the ratio of channel gain for anintended node to a sum of channel gains for all interfered nodes. Part(ii) may be equivalent to minimizing pathloss for the uplink but may bedifferent for the downlink. Maximizing user throughput may take intoaccount various factors such as the loading of a base station (e.g., thenumber of UEs currently served by the base station), the amount ofresources allocated to the base station, the available backhaul capacityof the base station, etc.

The wireless network may support a set of resources that may beavailable for transmission. The available resources may be defined basedon time, or frequency, or both time and frequency, or some othercriteria. For example, the available resources may correspond todifferent frequency subbands, or different time interlaces, or differenttime-frequency blocks, etc. A time interlace may include evenly spacedtime slots, e.g., every S-th time slot, where S may be any integervalue. The available resources may be defined for the entire wirelessnetwork.

The available resources may be used by base stations in the wirelessnetwork in various manners. In one scheme, each base station may use allof the available resources for transmission. This scheme may result insome base stations achieving poor performance. For example, femto basestation 110 d in FIG. 1 may be located within the vicinity of macro basestations 110 a and 110 b, and transmissions from femto base station 110d may observe high interference from macro base stations 110 a and 110b. In another scheme, the available resources may be allocated to basestations based on a fixed resource partitioning. Each base station maythen use its allocated resources for transmission. This scheme mayenable each base station to achieve good performance on its allocatedresources. However, some base stations may be allocated more resourcesthan required whereas some other base stations may require moreresources than allocated, which may lead to suboptimal performance forthe wireless network.

In an aspect, adaptive resource partitioning may be performed todynamically allocate the available resources to nodes so that goodperformance can be achieved. Resource partitioning may also be referredto as resource allocation, resource coordination, etc. For adaptiveresource partitioning on the downlink, the available resources may beallocated to nodes by assigning each node with a list of transmit PSDlevels that can be used by that node on the available resources.Adaptive resource partitioning may be performed in a manner to maximizea utility function. Adaptive resource partitioning is in contrast tofixed or static resource partitioning, which may allocate a fixed subsetof the available resources to each node.

In one design, adaptive resource partitioning may be performed in acentralized manner. In this design, a designated entity may receivepertinent information for UEs and nodes, compute metrics for resourcepartitioning, and select the best resource partitioning based on thecomputed metrics. In another design, adaptive resource partitioning maybe performed in a distributed manner by a set of nodes. In this design,each node may compute certain metrics and may exchange metrics withneighbor nodes. The metric computation and exchange may be performed forone or more rounds. Each node may then determine and select the resourcepartitioning that can provide the best performance.

Table 1 lists a set of components that may be used for adaptive resourcepartitioning.

TABLE 1 Component Description Active Set A set of nodes maintained for agiven UE t and denoted as AS(t). Neighbor Set A set of nodes maintainedfor a given node p and denoted as NS(p). Resources Time and/or frequencyresources that may be allocated to nodes. Transmit A set of transmit PSDlevels that may be used for any given PSD Levels resource by a node.Utility A function used to quantify the performance of differentFunction possible resource partitioning.

In one design, an active set may be maintained for each UE and may bedetermined based on pilot measurements made by the UE and/or pilotmeasurements made by nodes. An active set for a given UE t may includenodes that (i) have non-negligible contribution to signal orinterference observed by UE t on the downlink and/or (ii) receivenon-negligible signal or interference from UE t on the uplink. An activeset may also be referred to as an interference management set, acandidate set, etc.

In one design, an active set for UE t may be defined based on CoT, asfollows:

$\begin{matrix}{{{AS}(t)} = {\left\{ {q❘{\frac{{P(q)} \cdot {G\left( {q,t} \right)}}{N_{0}} > {CoT}_{\min}}} \right\}.}} & {{Eq}\mspace{14mu}(1)}\end{matrix}$where

P(q) is a transmit PSD of a pilot from node q,

G(q,t) is a channel gain between node q and UE t,

N₀ is ambient interference and thermal noise observed by UE t, and

CoT_(min) is a CoT threshold for selecting nodes to include in theactive set.

Equation (1) indicates that a given node q may be included in the activeset of UE t if the CoT of node q is greater than CoT_(min). The CoT ofnode q may be determined based on the transmit PSD of the pilot fromnode q, the channel gain between node q and UE t, and N₀. The pilot maybe a low reuse preamble (LRP) or a positioning reference signal, whichmay be transmitted on resources with low reuse and thus may bedetectable far away. The pilot may also be some other type of pilot orreference signal.

The active set of UE t may also be defined in other manners. Forexample, nodes may be selected based on received signal strength and/orother criteria instead of, or in addition to, received signal quality.The active set may be limited in order to reduce computation complexityfor adaptive resource partitioning. In one design, the active set may belimited to N nodes, where N may be any suitable value. The active setmay then include up to N strongest nodes with CoT exceeding CoT_(min).

In one design, a neighbor set may be maintained for each node and mayinclude nodes that participate in adaptive resource partitioning. Aneighbor set for a given node p may include neighbor nodes that (i)affect UEs served by node p or (ii) have UEs that can be affected bynode p. In one design, the neighbor set for node p may be defined asfollows:NS(p)={q|(∃t

p=S(t)&qεAS(t))|(∃t

q=S(t)&pεAS(t))},  Eq (2)where S(t) is a serving node for UE t.

Equation (2) indicates that a given node q may be included in theneighbor set of node p if (i) node q is in an active set of a UE that isserved by node p or (ii) node q is a serving node for a UE that has nodep in its active set. The neighbor set for each node may thus be definedbased on the active sets of UEs and their serving nodes. The neighborset may also be defined in other manners. Each node may be able todetermine its neighbor nodes based on the active sets of UEs served bythat node as well as information from the neighbor nodes. The neighborset may be limited in order to reduce computation complexity foradaptive resource partitioning.

FIG. 2 shows exemplary active sets for UEs and exemplary neighbor setsfor nodes in FIG. 1. The active set for each UE is shown withinparenthesis next to the UE in FIG. 2, with the serving node/base stationbeing underlined. For example, the active set for UE1 is {M1 , M2},which means that the active set includes serving node M1 and neighbornode M2. The neighbor set for each node is shown within brackets next tothe node in FIG. 2. For example, the neighbor set for node M1 is [M2,P1, P2, F1] and includes macro base station M2, pico base stations P1and P2, and femto base station F1.

In one design, a set of transmit PSD levels may be defined for each nodeand may include all transmit PSD levels that can be used by the node foreach resource. A node may use one of the transmit PSD levels for eachresource on the downlink. The usage of a given resource may be definedby the transmit PSD level selected/allowed for that resource. In onedesign, the set of transmit PSD levels may include a nominal PSD level,a low PSD level, a zero PSD level, etc. The nominal PSD level on allavailable resources may correspond to the maximum transmit power of thenode. The set of transmit PSD levels for the node may be dependent onthe power class of the node. In one design, the set of transmit PSDlevels for a given power class may be the union of the nominal PSDlevels of all power classes lower than or equal to this power class,plus zero PSD level. For example, a macro node may include a nominal PSDlevel of 43 dBm (for the macro power class), a low PSD level of 33 dBm(corresponding to the nominal PSD level for the pico power class), and azero PSD level. The set of transmit PSD levels for each power class mayalso be defined in other manners.

A utility function may be used to compute local metrics and overallmetrics for adaptive resource partitioning. The local metrics andoverall metrics may be used to quantify the performance of a givenresource partitioning. A local metric for a given node p may be denotedas U(p) and may be indicative of the performance of the node for a givenresource partitioning. An overall metric for a set of nodes, NS, may bedenoted as V(NS) and may be indicative of the overall performance of theset of nodes for a given resource partitioning. A local metric may alsobe referred to as a node metric, local utility, base station utility,etc. An overall metric may also be referred to as overall utility,neighborhood utility, etc. An overall metric may also be computed forthe entire wireless network. Each node may compute the local metrics andoverall metrics for different possible actions. The action thatmaximizes the utility function and yields the best overall metric may beselected for use.

In one design, the utility function may be defined based on a sum ofuser rates, as follows:

$\begin{matrix}{{{U(p)} = {{\sum\limits_{{S{(t)}} = p}{{R(t)}\mspace{14mu}{and}\mspace{14mu}{V({NS})}}} = {\sum\limits_{p \in {NS}}{U(p)}}}},} & {{Eq}\mspace{14mu}(3)}\end{matrix}$where R(t) is a rate achieved by UE t.

As shown in equation set (3), local metric U(p) for node p may be equalto the sum of rates achieved by all UEs served by node p. Overall metricV(NS) for neighbor set NS may be equal to the sum of the local metricsfor all nodes in the neighbor set. The utility function in equation (3)may not provide fairness guarantee.

In another design, the utility function may be defined based on aminimum user rate, as follows:

$\begin{matrix}{{U(p)} = {{\min\limits_{{S{(t)}} = p}{{R(t)}\mspace{14mu}{and}\mspace{14mu}{V({NS})}}} = {\min\limits_{p \in {NS}}{{U(p)}.}}}} & {{Eq}\mspace{14mu}(4)}\end{matrix}$

As shown in equation set (4), local metric U(p) for node p may be equalto the lowest rate achieved by all UEs served by node p. Overall metricV(NS) for neighbor set NS may be equal to the minimum of the localmetrics for all nodes in the neighbor set. The utility function inequation (4) may ensure equal grade of service (GoS) for all UEs, may beless sensitive to outliers, but may not provide trade off betweenfairness and sum throughput. In another design, an X % rate utilityfunction may be defined in which local metric U(p) for node p may be setequal to the highest rate of the lowest X % of all UEs served by node p,where X may be any suitable value.

In yet another design, the utility function may be defined based on asum of log of user rates, as follows:

$\begin{matrix}{{U(p)} = {{\sum\limits_{{S{(t)}} = p}{\log\;{R(t)}\mspace{14mu}{and}\mspace{14mu}{V({NS})}}} = {\sum\limits_{p \in {NS}}{{U(p)}.}}}} & {{Eq}\mspace{14mu}(5)}\end{matrix}$

As shown in equation set (5), local metric U(p) for node p may be equalto the sum of the log of the rates of all UEs served by node p. Overallmetric V(NS) for neighbor set NS may be equal to the sum of the localmetrics for all nodes in the neighbor set. The utility function inequation (5) may provide proportional fair scheduling.

In yet another design, the utility function may be defined based on asum of log of log of user rates, as follows:

$\begin{matrix}{{U(p)} = {{\sum\limits_{{S{(t)}} = p}{\log\;\left\{ {\log\;{R(t)}} \right\}\mspace{14mu}{and}\mspace{14mu}{V({NS})}}} = {\sum\limits_{p \in {NS}}{{U(p)}.}}}} & {{Eq}\mspace{14mu}(6)}\end{matrix}$

As shown in equation set (6), local metric U(p) for node p may be equalto the sum of the log of the log of the rates of all UEs served by nodep. Overall metric V(NS) for neighbor set NS may be equal to the sum ofthe local metrics for all nodes in the neighbor set. The utilityfunction in equation (6) may account for contributions from each UE andmay have more emphasis on tail distribution.

In yet another design, the utility function may be defined based on asum of −1/(user rate)³, as follows:

$\begin{matrix}{{U(p)} = {{\sum\limits_{{S{(t)}} = p}{\frac{- 1}{{R(t)}^{3}}\mspace{14mu}{and}\mspace{14mu} V({NS})}} = {\sum\limits_{p \in {NS}}{{U(p)}.}}}} & {{Eq}\mspace{14mu}(7)}\end{matrix}$

As shown in equation set (7), local metric U(p) for node p may be equalto the sum of minus one over the cube of the rates of all UEs served bynode p. Overall metric V(NS) for neighbor set NS may be equal to the sumof the local metrics for all nodes in the neighbor set. The utilityfunction in equation (7) may be more fair than proportional fair metric.

Equation sets (3) through (7) show some exemplary designs of the utilityfunction that may be used for adaptive resource partitioning. Theutility function may also be defined in other manners. The utilityfunction may also be defined based on other parameters instead of rateor in addition to rate. For example, the utility function may be definedbased on a function of rate, latency, queue size, etc.

For the designs shown in equation sets (3) through (7), the local metricfor each node may be computed based on the rates of UEs served by thatnode. In one design, the rate of each UE may be estimated by assumingthat the UE is assigned a fraction of each available resource. Thisfraction may be denoted as α(t,r) and may be viewed as the fraction oftime during which resource r is assigned to UE t. The rate for UE t maythen be computed as follows:

$\begin{matrix}{{{R(t)} = {\sum\limits_{r}{{\alpha\left( {t,r} \right)} \cdot {{SE}\left( {t,r} \right)} \cdot {W(r)}}}},} & {{Eq}\mspace{14mu}(8)}\end{matrix}$where SE(t,r) is the spectral efficiency of UE t on resource r, and

W(r) is the bandwidth of resource r.

The spectral efficiency of UE t on resource r may be determined asfollows:

$\begin{matrix}{{{{SE}\left( {t,r} \right)} = {C\left( \frac{{{PSD}\left( {p,r} \right)} \cdot {G\left( {p,t} \right)}}{N_{0} + {\sum\limits_{q \neq p}{{{PSD}\left( {q,r} \right)} \cdot {G\left( {q,t} \right)}}}} \right)}},} & {{Eq}\mspace{14mu}(9)}\end{matrix}$where

PSD(p,r) is the transmit PSD of serving node p on resource r,

PSD(q,r) is the transmit PSD of neighbor node q on resource r,

G(p,t) is the channel gain between serving node p and UE t, and

C( ) denotes a capacity function.

In equation (9), the numerator within the parenthesis denotes thedesired received power from serving node p at UE t. The denominatordenotes the total interference from all neighbor nodes as well as N₀ atUE t. The transmit PSD used by serving node p on resource r and thetransmit PSD used by each neighbor node on resource r may be known. Thechannel gains for serving node p and the neighbor nodes may be obtainedbased on pilot measurements from UE t. N₀ may be measured/estimated atUE t and included in the computation, or may be reported by UE t to thewireless network (e.g., to serving node p), or may be ignored (e.g.,when the computation is done at node p). The capacity function may be aconstrained capacity function, an unconstrained capacity function, orsome other function.

A pre-scheduler may maximize the utility function over the space of theα(t,r) parameters, as follows:

$\begin{matrix}{{{maximize}\mspace{14mu}{U(p)}},{{{for}\mspace{14mu} 0} \leq {\alpha\left( {t,r} \right)} \leq {1\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{t}{\alpha\left( {t,r} \right)}}} \leq 1},{and}} & {{Eq}\mspace{14mu}(10)} \\{{{U(p)} = {f\left( \left\{ {R(t)} \right\}_{{S{(t)}} = p} \right)}},} & {{Eq}\mspace{14mu}(11)}\end{matrix}$where f ( ) denotes a concave function of rates for all UEs served bynode p. Equation (10) shows a convex optimization on the α(t,r)parameters and may be solved numerically. The pre-scheduler may performscheduling forecast and may be different from an actual scheduler, whichmay maximize a marginal utility in each scheduling interval.

The rate for UE t may be constrained as follows:R(t)≦R _(max)(t)  Eq (12)where R_(max)(t) is the maximum rate supported by UE t.

The overall rate R(p) for node p may be constrained as follows:

$\begin{matrix}{{{R(p)} = {{\sum\limits_{{S{(t)}} = p}{R(t)}} \leq {R_{BH}(p)}}},} & {{Eq}\mspace{14mu}(13)}\end{matrix}$where R_(BH)(p) is a backhaul rate for node p. The backhaul rate may besent to neighbor nodes via the backhaul and/or may be sent over the airfor decisions to select serving nodes for UEs.

In one design, an adaptive algorithm may be used for adaptive resourcepartitioning. The algorithm is adaptive in that it can take intoconsideration the current operating scenario, which may be different fordifferent parts of the wireless network and may also change over time.The adaptive algorithm may be performed by each node in a distributedmanner and may attempt to maximize the utility function over a set ofnodes or possibly across the entire wireless network.

FIG. 3 shows a design of a process 300 for performing adaptive resourcepartitioning. Process 300 may be performed by each node in a neighborset for a distributed design. For clarity, process 300 is describedbelow for node p. Node p may obtain the current resource usage profileof each node in the neighbor set (step 312). For the downlink, aresource usage profile for a node may be defined by a set of transmitPSD levels, one transmit PSD level for each available resource. Node pmay obtain the current resource usage profiles of the neighbor nodes viathe backhaul or through other means.

Node p may determine a list of possible actions related to resourcepartitioning that can be performed by node p and/or neighbor nodes (step314). Each possible action may correspond to a specific resource usageprofile for node p as well as a specific resource usage profile for eachneighbor node in the neighbor set. For example, a possible action mayentail node p changing its transmit PSD on a particular resource and/ora neighbor node changing its transmit PSD on the resource. The list ofpossible actions may include (i) standard actions that may be evaluatedperiodically without any explicit request and/or (ii) on-demand actionsthat may be evaluated in response to requests from neighbor nodes. Somepossible actions are described below. The list of possible actions maybe denoted as A.

Node p may compute local metrics for different possible actions (block316). A local metric may indicate the performance of a node for a givenaction. For example, a local metric based on the utility function inequation (3) may indicate the overall rate achieved by node p for aparticular action a and may be computed as follows:

$\begin{matrix}{{{U\left( {p,a} \right)} = {\sum\limits_{{S{(t)}} = p}{R\left( {t,a} \right)}}},} & {{Eq}\mspace{14mu}(14)}\end{matrix}$where R(t,a) is the rate achieved by UE t on all available resources foraction a, and

U(p,a) is a local metric for node p for action a.

The rate R(t,a) for each UE may be computed as shown in equations (8)and (9), where PSD(p,r) and PSD(q,r) may be dependent on the resourceusage profiles for nodes p and q, respectively, associated with possibleaction a. In the design shown in equation (14), the rate for each UE onall available resources may first be determined, and the rates for allUEs served by node p may then be summed to obtain the local metric fornode p. In another design, the rate for each UE on each availableresource may first be determined, the rates for all UEs on eachavailable resource may next be computed, and the rates for all availableresources may then be summed to obtain the local metric for node p. Thelocal metric for node p for each possible action may also be computed inother manners and may be dependent on the utility function.

The local metrics for different possible actions may be used by node pas well as the neighbor nodes to compute overall metrics for differentpossible actions. Node p may send its computed local metrics U(p,a), foraεA, to the neighbor nodes (block 318). Node p may also receive localmetrics U(q,a), for aεA, from each neighbor node q in the neighbor set(block 320). Node p may compute overall metrics for different possibleactions based on its computed local metrics and the received localmetrics (block 322). For example, an overall metric based on the utilityfunction in equation (3) may be computed for each possible action a, asfollows:

$\begin{matrix}{{{V(a)} = {{U\left( {p,a} \right)} + {\sum\limits_{q \in {{{NS}{(p)}}\backslash{\{ p\}}}}{U\left( {q,a} \right)}}}},} & {{Eq}\mspace{14mu}(15)}\end{matrix}$where V(a) is an overall metric for possible action a. The summation inequation (15) is over all nodes in the neighbor set except for node p.

After completing the metric computation, node p may select the actionwith the best overall metric (block 324). Each neighbor node maysimilarly compute overall metrics for different possible actions and mayalso select the action with the best overall metric. Node p and theneighbor nodes should select the same action if they operate on the sameset of local metrics. Each node may then operate based on the selectedaction, without having to communicate with one another regarding theselected action. However, node p and its neighbor nodes may operate ondifferent local metrics and may obtain different best overall metrics.This may be the case, for example, if node p and its neighbor nodes havedifferent neighbor sets. In this case, node p may negotiate with theneighbor nodes to determine which action to take. This may entailexchanging overall metrics for some promising actions between the nodesand selecting the action that can provide good performance for as manynodes as possible.

Regardless of how the best action is selected, the selected action isassociated with a specific resource usage profile for node p. Node p mayutilize the available resources in accordance with the resource usageprofile associated with the selected action (block 326). This resourceusage profile may be defined by a specific list of transmit PSD levels,one transmit PSD level for each available resource. Node p may then usethe specified transmit PSD level for each available resource.

There may be a large number of possible actions to evaluate for anexhaustive search to find the best action. In particular, if there are Lpossible transmit PSD levels for each resource, K available resources,and N nodes in the neighbor set, then the total number of possibleactions, T, may be given as T=L^(K·N). Evaluating all T possible actionsmay be computationally intensive.

The number of possible actions to evaluate may be reduced in variousmanners. In one design, each available resource may be treatedindependently, and a given action may change the transmit PSD of onlyone resource. The number of possible actions may then be reduced toT=(L^(N))·K. In another design, the number of nodes that can adjusttheir transmit PSD on a given resource for a given action may be limitedto Nx, which may be less than N. The number of possible actions may thenbe reduced to T=(L^(Nx))·K. In yet another design, the transmit PSD fora given resource may be either increased or decreased by one level at atime. The number of possible actions may then be reduced toT=(2^(Nx))·K. The number of possible actions may also be reduced viaother simplifications.

In one design, a list of possible actions that may lead to good overallmetrics may be evaluated. Possible actions that are unlikely to providegood overall metrics may be skipped in order to reduce computationcomplexity. For example, having both node p and a neighbor node increasetheir transmit PSD on the same resource will likely result in extrainterference on the resource, which may degrade performance for bothnodes. This possible action may thus be skipped.

Table 2 lists different types of actions that may be evaluated foradaptive resource partitioning, in accordance with one design.

TABLE 2 Action Types Action Type Description p-C-r Node p claimsresource r and increases its transmit PSD by one level on resource r.p-B-r Node p blanks resource r and decreases its transmit PSD by onelevel on resource r. p-R-r-Q Node p requests resource r from one or moreneighbor nodes in set Q and asks the neighbor node(s) in set Q todecrease their transmit PSD by one level on resource r. p-G-r-Q Node pgrants resource r to one or more neighbor nodes in set Q and tells theneighbor node(s) in set Q to increase their transmit PSD by one level onresource r. p-CR-r-Q Node p claims and requests resource r from one ormore neighbor nodes in set Q and (i) increases its transmit PSD by onelevel on resource r and (ii) asks the neighbor node(s) in set Q todecrease their transmit PSD by one level on resource r. p-BG-r-Q Node pblanks and grants resource r to one or more neighbor nodes in set Q and(i) decreases its transmit PSD by one level on resource r and (ii) tellsthe neighbor node(s) in set Q to increase their transmit PSD by onelevel on resource r.

Each action type in Table 2 may be associated with a set of possibleactions of that type. For each action type involving only node p, Kpossible actions may be evaluated for the K available resources. Foreach action type involving both node p and one or more neighbor nodes inset Q, multiple possible actions may be evaluated for each availableresource, with the number of possible actions being dependent on thesize of the neighbor set, the size of set Q, etc. In general, set Q mayinclude one or more neighbor nodes and may be limited to a small value(e.g., 2 or 3) in order to reduce the number of possible actions toevaluate.

Node p may compute a local metric for each possible action of eachaction type. Table 3 lists some local metrics that may be computed bynode p for different types of actions listed in Table 2. The localmetrics in Table 3 are for different possible actions on a givenresource r. This coincides with the design in which each possible actionis limited to one resource in order to reduce computation complexity.

TABLE 3 Local metrics Local metric Description U_(I)(p, r) Local metricfor node p if it increases its transmit PSD on resource r by one level.U_(D)(p, r) Local metric for node p if it decreases its transmit PSD onresource r by one level. U_(0/I)(p, q, r) Local metric for node p ifneighbor node q increases its transmit PSD on resource r by one level.U_(0/D)(p, q, r) Local metric for node p if neighbor node q decreasesits transmit PSD on resource r by one level. U_(I/D)(p, q, r) Localmetric for node p if it increases its transmit PSD on resource r by onelevel and neighbor node q decreases its transmit PSD on resource r byone level. U_(D/I)(p, q, r) Local metric for node p if it decreases itstransmit PSD on resource r by one level and neighbor node q increasesits transmit PSD on resource r by one level. U_(0/I/D)(p, n, Q, r) Localmetric for node p if neighbor node n increases its transmit PSD onresource r by one level and one or more neighbor nodes in set Q decreasetheir transmit PSD on resource r by one level. U_(0/D/I)(p, n, Q, r)Local metric for node p if neighbor node n decreases its transmit PSD onresource r by one level and one or more neighbor nodes in set Q increasetheir transmit PSD on resource r by one level.

Local metrics U_(0/I)(p,Q,r), U_(0/D)(p,Q,r), U_(I/D)(p,Q,r) andU_(D/I)(p,Q,r) for a set of neighbor nodes, Q, may be defined in similarmanner as local metrics U_(0/I)(p,q,r), U_(0/D)(p,q,r), U_(I/D)(p,q,r)and U_(D/I)(p,q,r), respectively, for a single neighbor node q. Forexample, U_(0/I)(p,Q,r) may be the local metric for node p if allneighbor nodes in set Q increases their transmit PSD on resource r byone level.

Node p may compute local metrics for different possible actions based on(i) pilot measurements from UEs having node p in their active sets and(ii) the resource usage profiles for node p and neighbor nodesassociated with these possible actions. For each possible action, node pmay first compute the spectral efficiency SE(t,r) of each UE served bynode p on each resource r, e.g., as shown in equation (9). Thecomputation of the spectral efficiency R(t,r) may be dependent on ascheduling forecast to obtain the α(t,r) values for the UEs. PSD(p,r)and PSD(q,r) in equation (9) may be obtained from the resource usageprofiles for nodes p and q, respectively. G(p,t) and G(q,t) is equation(9) may be obtained from pilot measurements from UE t for nodes p and q,respectively. A local metric for the possible action may then becomputed based on the rates for all UEs on all available resources,e.g., as shown in equation (3) for the sum rate utility function.

The computation of the local metrics makes use of pilot measurementsthat are limited to nodes in the active sets of the UEs. Therefore, theaccuracy of the local metrics may be affected by the CoT_(min) thresholdused to select nodes for inclusion in active sets, e.g., as shown inequation (1). A higher CoT_(min) threshold may correspond to higheramount of ambient interference and lower accuracy of the local metrics.A higher CoT_(min) threshold also corresponds to more relaxedrequirements on UE measurement capability and a smaller active set. TheCoT_(min) threshold may be selected based on a trade off between UErequirements and complexity on one hand and metric computation accuracyon the other hand.

Node p may exchange local metrics with the neighbor nodes in theneighbor set (e.g., via the backhaul) to enable each node to computeoverall metrics for different possible actions. In one design, localmetrics for possible actions involving only node p (e.g., the first twolocal metrics in Table 3) may be sent to all neighbor nodes in theneighbor set. Local metrics for possible actions involving neighbor nodeq (e.g., the middle four local metrics in Table 3) may be sent to onlynode q. Local metrics for possible actions involving neighbor nodes inset Q (e.g., the last two local metrics in Table 3) may be sent to eachnode in set Q.

In one design, some local metrics (e.g., the first six local metrics inTable 3) may be computed periodically and exchanged between the nodes inthe neighbor set, e.g., via standard resource negotiation messages. Inone design, remaining local metrics (e.g., the last two local metrics inTable 3 and local metrics for set Q) may be computed when requested andexchanged via on-demand messages. The local metrics may be computed andexchanged between nodes in other manners.

Node p may compute local metrics for different possible actions and mayalso receive local metrics for different possible actions from neighbornodes. Node p may compute overall metrics for different possible actionsbased on the computed local metrics and the received local metrics.Table 4 lists some overall metrics that may be computed by node p fordifferent types of actions listed in Table 2.

TABLE 4 Overall Metrics Overall Metric Description V_(C)(p, r) Overallmetric for a p-C-r action on resource r. V_(B)(p, r) Overall metric fora p-B-r action on resource r. V_(R)(p, Q, r) Overall metric for ap-R-r-Q action on resource r. V_(G)(p, Q, r) Overall metric for ap-G-r-Q action on resource r. V_(CG)(p, Q, r) Overall metric for ap-CG-r-Q action on resource r. V_(BG)(p, Q r) Overall metric for ap-BG-r-Q action on resource r.

For clarity, the description below assumes a utility function in whichan overall metric of a neighbor set for a possible action is equal tothe sum of local metrics of all nodes in the neighbor set for thepossible action. The computation of the overall metric may be modifiedaccordingly for other types of utility function. For example, asummation for the overall metric may be replaced with a minimumoperation for a utility function that minimizes a particular parameter.

In one design, an overall metric for a p-C-r action may be computed asfollows:

$\begin{matrix}{{{V_{C}\left( {p,r} \right)} = {{U_{I}\left( {p,r} \right)} + {\sum\limits_{q \in {{{NS}{(p)}}\backslash{\{ p\}}}}{U_{0/I}\left( {q,p,r} \right)}}}},{and}} & {{Eq}\mspace{14mu}(16)} \\{{{\Delta\;{V_{C}\left( {p,r} \right)}} = {{V_{C}\left( {p,r} \right)} - {V\left( {{NS}(p)} \right)}}},} & {{Eq}\mspace{14mu}(17)}\end{matrix}$where ΔV_(C)(p,r) is a change in the overall metric for the p-C-raction, and

V(NS(p)) is an overall metric for the current resource usage by theneighbor set.

As shown in equation (16), overall metric V_(C)(p,r) may be computedbased on local metric U_(I)(p,r) computed by node p and local metricU_(0/I)(q,p,r) received from neighbor nodes. As shown in equation (17),the change in the overall metric may be computed and used instead of theabsolute value from equation (16).

In one design, an overall metric for a p-B-r action may be computed asfollows:

$\begin{matrix}{{{{V_{B}\left( {p,r} \right)} = {{U_{D}\left( {p,r} \right)} + {\sum\limits_{q \in {{{NS}{(p)}}\backslash{\{ p\}}}}{U_{0/D}\left( {q,p,r} \right)}}}},{and}}\;} & {{Eq}\mspace{14mu}(18)} \\{{{\Delta\;{V_{B}\left( {p,r} \right)}} = {{V_{B}\left( {p,r} \right)} - {V\left( {{NS}(p)} \right)}}},} & {{Eq}\mspace{14mu}(19)}\end{matrix}$where ΔV_(B)(p,r) is a change in the overall metric for the p-B-raction.

As shown in equation (18), overall metric V_(B)(p,r) may be computedbased on local metrics U_(D)(p,r) computed by node p and local metricsU_(0/D)(q,p,r) received from neighbor nodes. Node p may exchange overallmetrics V_(C)(p,r) and V_(B)(p,r) (or the corresponding ΔV_(C)(p,r) andΔV_(B)(p,r)) with neighbor nodes for use in computing other overallmetrics.

In one design, an overall metric for a p-G-r-Q action may be computed asfollows. First, an initial estimate of the overall metric may becomputed as follows:

$\begin{matrix}{{{V_{G,0}\left( {p,Q,r} \right)} = {{U_{0/I}\left( {p,Q,r} \right)} + {\sum\limits_{q \in Q}\left\{ {{V_{C}\left( {q,r} \right)} - {U_{0/I}\left( {p,q,r} \right)}} \right\}}}},{and}} & {{Eq}\mspace{14mu}(20)} \\{{{{\Delta V}_{G,0}\left( {p,Q,r} \right)} = {{V_{G,0}\left( {p,Q,r} \right)} - {U(p)} - {\sum\limits_{q \in Q}\left\{ {{V\left( {{NS}(q)} \right)} - {U(p)}} \right\}}}},} & {{Eq}\mspace{14mu}(21)}\end{matrix}$where

U(p) is a local metric for node p for the current resource usage,

V_(G,0)(p,Q,r) is an initial estimate of the overall metric for ap-G-r-Q action, and

ΔV_(G,0)(p,Q,r) is an initial estimate of the change in the overallmetric.

As shown in equation (20), V_(G,0)(p,Q,r) may be computed based on localmetrics U_(0/I)(p,q,r) and U_(0/I)(p,Q,r) computed by node p and overallmetrics V_(C)(q,r) received from neighbor nodes. If the initial estimateseems promising (e.g., if the change in the overall metric is largerthan a threshold), then the overall metric may be more accuratelycomputed as follows:

$\begin{matrix}{{{V_{G}\left( {p,Q,r} \right)} = {{\sum\limits_{n \in {{NS}{(p)}}}{U_{0/I}\left( {n,Q,r} \right)}} + {\sum\limits_{q \in Q}\left( {{V_{C}\left( {q,r} \right)} - {\sum\limits_{n \in {N\; 1}}{U_{0/I}\left( {n,q,r} \right)}}} \right)}}},} & {{Eq}\mspace{14mu}(22)} \\{{{\Delta\;{V_{G}\left( {p,Q,r} \right)}} = {{V_{G}\left( {p,Q,r} \right)} - {V\left( {{NS}(p)} \right)} - {\sum\limits_{q \in Q}\left( {{V\left( {{NS}(q)} \right)} - {\sum\limits_{n \in {N\; 1}}{U(n)}}} \right)}}},} & {{Eq}\mspace{14mu}(23)}\end{matrix}$where ΔV_(G)(p,Q,r) is the change in the overall metric for the p-G-r-Qaction, and

N1=NS(p)∩NS(q).

In one design, node p may request for local metrics U_(0/I)(n,q,r) andU_(0/I)(n,Q,r) in equation (22) from the neighbor nodes only if theinitial estimate seems promising. This design may reduce the amount ofinformation to exchange via the backhaul for adaptive resourcepartitioning.

In one design, an overall metric for a p-R-r-Q action may be computed insimilar manner as an overall metric for a p-G-r-Q action. Equations (18)to (21) may be used to compute the overall metric for the p-R-r-Qaction, albeit with local metrics U_(0/I)(p,q,r), U_(0/I)(p,Q,r),U_(0/I)(n,q,r) and U_(0/I) (n,Q,r) being replaced with local metricsU_(0/D)(p,q,r), U_(0/D)(p,Q,r), U_(0/D)(n,q,r) and U_(0/D) (n,Q,r),respectively.

In one design, an overall metric for a p-BG-r-Q action may be computedas follows. First, an initial estimate of the overall metric may becomputed as follows:

$\begin{matrix}{{{V_{{BG},0}\left( {p,Q,r} \right)} = {{U_{D/I}\left( {p,Q,r} \right)} + {\sum\limits_{n \in {N\; 2}}{U_{0/D}\left( {n,p,r} \right)}} + {\sum\limits_{q \in Q}\left\{ {{U_{I/D}\left( {q,p,r} \right)} + {V_{C}\left( {q,r} \right)} - {U_{I}\left( {q,r} \right)} - {U_{0/I}\left( {p,q,r} \right)}} \right\}}}},} & {{Eq}\mspace{14mu}(24)} \\{{\Delta\;{V_{{BG},0}\left( {p,Q,r} \right)}} = {{V_{{BG},0}\left( {p,Q,r} \right)} - {V\left( {{NS}(p)} \right)} - {\sum\limits_{q \in Q}\left\{ {{V\left( {{NS}(q)} \right)} - {U(p)} - {U(q)}} \right\}}}} & {{Eq}\mspace{14mu}(25)}\end{matrix}$where

V_(BG,0)(p,Q,r) is an initial estimate of the overall metric for ap-BG-r-Q action,

ΔV_(BG,0)(p,Q,r) is an initial estimate of the change in the overallmetric, and

N2=NS(p)\(Q∪{p}).

As shown in equation (24), V_(BG,0)(p,Q,r) may be computed based on (i)local metrics U_(0/I)(p,q,r) and U_(D/I)(p,Q,r) computed by node p and(ii) local metrics U_(I)(q,r), U_(0/D)(n,p,r) and U_(I/D)(q,p,r) andoverall metric V_(C)(q,r) received from neighbor nodes. If the initialestimate seems promising, then the overall metric may be more accuratelycomputed as follows:

$\begin{matrix}{{{V_{BG}\left( {p,Q,r} \right)} = {{\sum\limits_{n \in {{NS}{(p)}}}{U_{{0/D}/I}\left( {n,p,Q,r} \right)}} + {\sum\limits_{q \in Q}\left( {{V_{C}\left( {q,r} \right)} - {\sum\limits_{n \in {N\; 1}}{U_{0/I}\left( {n,q,r} \right)}}} \right)}}},} & {{Eq}\mspace{14mu}(26)} \\{{{\Delta\;{V_{BG}\left( {p,Q,r} \right)}} = {{V_{GB}\left( {p,Q,r} \right)} - {V\left( {{NS}(p)} \right)} - {\sum\limits_{q \in Q}\left( {{V\left( {{NS}(q)} \right)} - {\sum\limits_{n \in {N\; 1}}{U(n)}}} \right)}}},} & {{Eq}\mspace{14mu}(27)}\end{matrix}$where ΔV_(BG)(p,Q,r) is a change in the overall metric for the p-BG-r-Qaction. Node p may request for local metrics U_(0/I)(n,q,r) andU_(0/D/I)(n,p,Q,r) in equation (26) from the neighbor nodes if theinitial estimate seems promising.

In one design, an overall metric for a p-CR-r-Q action may be computedin similar manner as an overall metric for a p-BG-r-Q action. Equations(24) to (27) may be used to compute the overall metric for the p-CR-r-Qaction, e.g., with local metrics U_(0/I)(n,q,r) and U_(0/D/I)(n,p,Q,r)in equation (26) being replaced with U_(0/D)(n,q,r) andU_(0/I/D)(n,p,Q,r), respectively.

Equations (16) through (27) show exemplary computations for the overallmetrics in Table 4, which are for the different types of actions inTable 2. Some overall metrics may be computed based solely on localmetrics, e.g., as shown in equations (16) and (18). Some other overallmetrics may be computed based on a combination of local metrics andoverall metrics, e.g., as shown in equations (22) and (26). The use ofsome overall metrics to compute other overall metrics may simplifycomputation. In general, an overall metric may be computed based solelyon local metrics or based on both local metrics and other overallmetrics. The nodes may exchange local metrics and/or overall metrics viaone or more rounds of messages.

The overall metrics may also be computed in other manners, e.g., basedon other equations, other local metrics, etc. In general, any set ofaction types may be supported. The overall metrics may be computed forthe support action types and may be defined in various manners.

Adaptive resource partitioning for a small wireless network with nodesof two power classes was simulated. In the simulation, a neighbor setincludes two nodes for macro base stations (or macro nodes) and sixnodes for pico base stations (or pico nodes). Each macro node has threePSD levels—a nominal PSD level of 43 dBm (denoted as 2), a low PSD levelof 33 dBm (denoted as 1), and zero PSD level (denoted as 0). Each piconode has two PSD levels—a nominal PSD level of 33 dBm (denoted as 1) andzero PSD level (denoted as 0). A total of four resources are availablefor partitioning between the nodes. A total of 16 UEs are distributedthroughout the wireless network.

FIG. 4 shows the wireless network in the simulation. The two macro nodesare denoted as M1 and M2, the four pico nodes are denoted as P1 throughP4, and the 16 UEs are denoted as UE1 through UE16. FIG. 4 also showsthe result of the adaptive resource partitioning based on the adaptivealgorithm described above. Next to each node is a set of four numbersindicative of the transmit PSD levels on the four available resourcesfor the node. For example, macro node M2 is associated with ‘0211’,which means that zero transmit PSD is used on resource 1, 43 dBm is usedon resource 2, 33 dBm is used on resource 3, and 33 dBm is used onresource 4.

FIG. 4 also shows a communication link between each UE and its servingnode. The communication link for each UE is labeled with two numbers.The top number indicates the total fraction of the resources assigned tothe UE. The bottom number indicates the total rate R(t) achieved by theUE. For example, the communication link from UE9 to macro node M2indicates that UE9 is assigned 2.2 out of three resources on average andachieves a rate of 3.9 Mbps. For each node, the sum of the resourcesassigned to all UEs served by that node should be equal to the resourcesallocated to the node by the adaptive resource partitioning.

Table 5 lists the performance of adaptive resource partitioning as wellas the performance of a number of fixed resource partitioning schemes.For a fixed X:Y partitioning, X resources are allocated to macro nodes,and Y resources are allocated to pico nodes, and each node uses thenominal PSD level on each resource allocated to that node, where X+Y=4for the example shown in FIG. 4. For the adaptive resource partitioning,each node may be allocated a configurable number of resources, and eachmacro node may transmit at 43 dBm or 33 dBm on each allocated resource.

Table 5 shows three overall metrics for the different resourcepartitioning schemes. A log log IU overall metric is based on theutility function shown in equation (6). A minimum rate overall metric(Rmin) is based on the utility function shown in equation (4). A sumrate overall metric (Rsum) is based on the utility function shown inequation (3). As shown in Table 5, the adaptive resource partitioningmay provide better performance than the fixed resource partitioningschemes.

TABLE 5 Resource Partitioning Scheme log log IU Rmin Rsum Units AdaptiveResource Partitioning 6.37 3.29 119.64 Mbps Fixed 1:3 Partitioning 4.851.73 92.81 Mbps Fixed 2:2 Partitioning 4.23 1.15 87.56 Mbps Fixed 3:1Partitioning 2.72 0.58 82.33 Mbps

In one design, adaptive resource partitioning may be performed for allresources available for transmission in a wireless network. In anotherdesign, adaptive resource partitioning may be performed for a subset ofthe available resources. For example, macro nodes may be allocated afirst subset of resources, and pico nodes may be allocated a secondsubset of resources based on fixed resource partitioning. The remainingavailable resources may be dynamically allocated to the macro nodes orpico nodes based on adaptive resource partitioning. For the exampleshown in FIG. 4, the macro nodes may be assigned one resource, the piconodes may be assigned one resource, and two remaining resources may bedynamically allocated to the macro nodes or the pico nodes based onadaptive resource partitioning. This design may reduce computationcomplexity.

For clarity, adaptive resource partitioning for the downlink has beendescribed above. Adaptive resource partitioning for the uplink may beperformed in a similar manner. In one design, a set of targetinterference-over-thermal (IoT) levels may be used for resourcepartitioning on the uplink in similar manner as the set of PSD levelsfor the downlink. One target IoT level may be selected for each resourceon the uplink, and transmissions from each UE on each resource may becontrolled so that the actual IoT on that resource at each neighbor nodein the active set of the UE is at or below the target IoT level for thatresource at the neighbor node. A utility function may be defined toquantify performance of data transmission on the uplink and may be afunction of sum of user rates, or minimum of user rates, etc. The rateof each UE on the uplink may be a function of transmit power, channelgain, and target IoT level, etc. Local metrics and overall metrics maybe computed for different possible actions based on the utilityfunction. Each possible action may be associated with a list of targetIoT levels for all available resources for each node in a neighbor set.The possible action with the best overall metric may be selected foruse.

FIG. 5 shows a design of a process 500 for supporting communication.Process 500 may be performed by a node (as described below) or by someother entity (e.g., a network controller). The node may be a basestation, a relay, or some other entity. The node may obtain overallmetrics for a plurality of possible actions related to resourcepartitioning to allocate available resources to a set of nodes thatincludes the node (block 512). Each possible action may be associatedwith a set of resource usage profiles for the set of nodes, one resourceusage profile for each node. Each resource usage profile may indicateallowed usage of the available resources by a particular node. The nodemay determine allocation of the available resources to the set of nodesbased on the overall metrics for the plurality of possible actions(block 514).

The available resources may be for time units, frequency units,time-frequency units, etc. In one design, the available resources may befor the downlink. In this design, each node in the set of nodes may beassociated with a set of transmit PSD levels allowed for that node. Eachresource usage profile may comprise a list of transmit PSD levels forthe available resources, one transmit PSD level for each availableresource. The transmit PSD level for each available resource may be oneof the set of transmit PSD levels. In another design, the availableresources may be for the uplink. In this design, each resource usageprofile may comprise a list of target IoT levels for the availableresources, one target IoT level for each available resource.

In one design of block 514, the node may select one of the plurality ofpossible actions based on the overall metrics for these possibleactions. The node may determine resources allocated to the node based ona resource usage profile associated with the selected action andapplicable for the node. The node may schedule data transmission for atleast one UE on the available resources based on the resource usageprofile for the node.

FIG. 6 shows a design of an apparatus 600 for supporting communication.Apparatus 600 includes a module 612 to obtain overall metrics for aplurality of possible actions related to resource partitioning toallocate available resources to a set of nodes, and a module 614 todetermine allocation of the available resources to the set of nodesbased on the overall metrics for the plurality of possible actions.

FIG. 7 shows a design of a process 700 for performing adaptive resourcepartitioning, which may be used for blocks 512 and 514 in FIG. 5. A nodemay compute local metrics for a plurality of possible actions related toresource partitioning to allocate available resources to a set of nodesthat includes the node (block 712). The node may send the computed localmetrics to at least one neighbor node in the set of nodes to enable theneighbor node(s) to compute overall metrics for the plurality ofpossible actions (block 714). The node may receive local metrics for theplurality of possible actions from the at least one neighbor node (block716). The node may determine overall metrics for the plurality ofpossible actions based on the computed local metrics and the receivedlocal metrics for these possible actions (block 718). A local metric fora possible action may be indicative of the performance achieved by anode for the possible action. An overall metric for a possible actionmay be indicative of the overall performance achieved by the set ofnodes for the possible action.

The node may select one of the plurality of possible actions based onthe overall metrics for the plurality of possible actions, e.g., selectthe action with the best overall metric (block 720). The node mayutilize the available resources based on a resource usage profileassociated with the selected action and applicable for the node (block722).

In one design of block 712, for each possible action, the node maydetermine at least one rate for at least one UE communicating with thenode based on (i) the set of resource usage profiles associated with thepossible action and (ii) channel gains between each UE and the node aswell as the neighbor node(s). The node may then determine a local metricfor the possible action based on the at least one rate. The localmetrics for the plurality of possible actions may be computed based on afunction of rate, or latency, or queue size, or some other parameter, ora combination thereof. The local metrics for the plurality of possibleactions may also be computed based on a function of sum of rates, orminimum of rates, or sum of quantities determined based on rates, etc.

In one design of blocks 714 and 716, a first subset of the computedlocal metrics and a first subset of the received local metrics may beexchanged between the node and the at least one neighbor nodeperiodically. A second subset of the computed local metrics and a secondsubset of the received local metrics may be exchanged between the nodeand the at least one neighbor node when requested.

In one design of block 718, for each possible action, the node maycombine a local metric computed by the node for the possible action withat least one local metric received from the at least one neighbor nodefor the possible action to obtain an overall metric for that possibleaction.

In one design, each of the plurality of possible actions may affect onlyone of the available resources. In another design, each possible actionmay change transmit PSD (or target IoT) by at most one level for anygiven node in the set of nodes. In one design, a set of action types maybe supported, e.g., as shown in Table 2. Each of the plurality ofpossible actions may be of one of the set of action types. The pluralityof possible actions may comprise (i) first possible actions for the nodeincreasing its transmit PSD, (ii) second possible actions for the nodedecreasing its transmit PSD, (iii) third possible actions for one ormore neighbor nodes increasing their transmit PSD, (iv) fourth possibleactions for the one or more neighbor nodes decreasing their transmitPSD, (v) fifth possible actions for the node increasing its transmit PSDand the one or more neighbor nodes decreasing their transmit PSD, (vi)sixth possible actions for the node decreasing its transmit PSD and theone or more neighbor nodes increasing their transmit PSD, or (vii) acombination thereof.

In one design, each UE may be associated with an active set of nodeshaving received signal quality or received signal strength above athreshold. The set of nodes may be determined based on active sets ofUEs and may include (i) nodes in active sets of UEs communicating withthe node and/or (ii) nodes serving UEs having active sets that includethe node. In one design, the set of nodes may include nodes of differentpower classes. For example, the set may include a first node with afirst maximum transmit power level and a second node with asecond/different maximum transmit power level. In another design, theset of nodes may include nodes of the same power class.

The description above is for a distributed design in which the nodes inthe set of nodes may each compute and exchange local metrics and overallmetrics for different possible actions. For a centralized design, adesignated entity may compute local metrics and overall metrics fordifferent possible actions and may select the best action.

FIG. 8 shows a design of a process 800 for communicating in a wirelessnetwork with adaptive resource partitioning. Process 800 may beperformed by a UE (as described below) or by some other entity. The UEmay make pilot measurements for nodes detectable by the UE (block 812).The pilot measurements may be used to determine an active set for theUE. The pilot measurements may also be used to compute local metrics foradaptive resource partitioning.

The UE may receive an assignment of at least one resource from a node(block 814). Adaptive resource partitioning may be performed to allocateavailable resources to a set of nodes that includes the node. The nodemay be allocated a subset of the available resources by the adaptiveresource partitioning. The at least one resource assigned to the UE maybe from the subset of the available resources allocated to the node.

The UE may communicate with the node on the at least one resource (block816). In one design of block 816, the UE may receive data transmissionon the at least one resource from the node. The data transmission may besent by the node on each of the at least one resource at a transmit PSDlevel allowed for the node on the resource. In another design of block816, the UE may send data transmission on the at least one resource tothe node. The data transmission may be sent by the UE on each of the atleast one resource at a transmit power level determined based on atleast one target IoT level for at least one neighbor node on theresource.

FIG. 9 shows a design of an apparatus 900 for communicating in awireless network with adaptive resource partitioning. Apparatus 900includes a module 912 to make pilot measurements for nodes detectable bya UE, a module 914 to receive an assignment of at least one resourcefrom a node at the UE, and a module 916 to communicate with the node bythe UE on the at least one resource.

The modules in FIGS. 6 and 9 may comprise processors, electronicdevices, hardware devices, electronic components, logical circuits,memories, software codes, firmware codes, etc., or any combinationthereof.

FIG. 10 shows a block diagram of a design of a base station/node 110 anda UE 120, which may be one of the base stations and one of the UEs inFIG. 1. Base station 110 may be equipped with T antennas 1034 a through1034 t, and UE 120 may be equipped with R antennas 1052 a through 1052r, where in general T≧1 and R≧1.

At base station 110, a transmit processor 1020 may receive data from adata source 1012 for one or more UEs and control information from acontroller/processor 1040. Processor 1020 may process (e.g., encode,interleave, and modulate) the data and control information to obtaindata symbols and control symbols, respectively. Processor 1020 may alsogenerate pilot symbols for pilot or reference signal. A transmit (TX)multiple-input multiple-output (MIMO) processor 1030 may perform spatialprocessing (e.g., precoding) on the data symbols, the control symbols,and/or the pilot symbols, if applicable, and may provide T output symbolstreams to T modulators (MODs) 1032 a through 1032 t. Each modulator1032 may process a respective output symbol stream (e.g., for OFDM,etc.) to obtain an output sample stream. Each modulator 1032 may furtherprocess (e.g., convert to analog, amplify, filter, and upconvert) theoutput sample stream to obtain a downlink signal. T downlink signalsfrom modulators 1032 a through 1032 t may be transmitted via T antennas1034 a through 1034 t, respectively.

At UE 120, antennas 1052 a through 1052 r may receive the downlinksignals from base station 110 and may provide received signals todemodulators (DEMODs) 1054 a through 1054 r, respectively. Eachdemodulator 1054 may condition (e.g., filter, amplify, downconvert, anddigitize) its received signal to obtain input samples. Each demodulator1054 may further process the input samples (e.g., for OFDM, etc.) toobtain received symbols. A MIMO detector 1056 may obtain receivedsymbols from all R demodulators 1054 a through 1054 r, perform MIMOdetection on the received symbols if applicable, and provide detectedsymbols. A receive processor 1058 may process (e.g., demodulate,deinterleave, and decode) the detected symbols, provide decoded data forUE 120 to a data sink 1060, and provide decoded control information to acontroller/processor 1080.

On the uplink, at UE 120, a transmit processor 1064 may receive andprocess data from a data source 1062 and control information fromcontroller/processor 1080. Processor 1064 may also generate pilotsymbols for pilot or reference signal. The symbols from transmitprocessor 1064 may be precoded by a TX MIMO processor 1066 ifapplicable, further processed by modulators 1054 a through 1054 r (e.g.,for SC-FDM, OFDM, etc.), and transmitted to base station 110. At basestation 110, the uplink signals from UE 120 may be received by antennas1034, processed by demodulators 1032, detected by a MIMO detector 1036if applicable, and further processed by a receive processor 1038 toobtain decoded data and control information sent by UE 120. Processor1038 may provide the decoded data to a data sink 1039 and the decodedcontrol information to controller/processor 1040.

Controllers/processors 1040 and 1080 may direct the operation at basestation 110 and UE 120, respectively. A channel processor 1084 may makepilot measurements, which may be used to determine an active set for UE120 and to compute channel gains, rates, metrics, etc. Processor 1040and/or other processors and modules at base station 110 may perform ordirect process 300 in FIG. 3, process 500 in FIG. 5, process 700 in FIG.7, and/or other processes for the techniques described herein. Processor1080 and/or other processors and modules at UE 120 may perform or directprocess 800 in FIG. 8 and/or other processes for the techniquesdescribed herein. Memories 1042 and 1082 may store data and programcodes for base station 110 and UE 120, respectively. A scheduler 1044may schedule UEs for data transmission on the downlink and/or uplink.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the disclosure herein may be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The various illustrative logical blocks, modules, and circuits describedin connection with the disclosure herein may be implemented or performedwith a general-purpose processor, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA) or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor may be a microprocessor, but in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thedisclosure herein may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Anexemplary storage medium is coupled to the processor such that theprocessor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anASIC. The ASIC may reside in a user terminal. In the alternative, theprocessor and the storage medium may reside as discrete components in auser terminal.

In one or more exemplary designs, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by ageneral purpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of instructions or datastructures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above should also beincluded within the scope of computer-readable media.

The previous description of the disclosure is provided to enable anyperson skilled in the art to make or use the disclosure. Variousmodifications to the disclosure will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other variations without departing from the spirit or scopeof the disclosure. Thus, the disclosure is not intended to be limited tothe examples and designs described herein but is to be accorded thewidest scope consistent with the principles and novel features disclosedherein.

What is claimed is:
 1. A method for wireless communication, comprising:determining a plurality of resource partitioning actions related toallocation of available resources among a set of base stations, eachresource partitioning action being associated with a set of resourceusage profiles for the set of base stations, one resource usage profilefor each base station, each resource usage profile indicating allowedusage of the available resources by a particular base station; computinglocal metrics for the plurality of resource partitioning actions by abase station of the set of base stations; receiving, by the basestation, local metrics for the plurality of resource partitioningactions from at least one neighbor base station in the set of basestations; obtaining overall metrics for the plurality of resourcepartitioning actions, the overall metrics being based at least in parton the computed local metrics and the received local metrics for theplurality of resource partitioning actions; and determining allocationof the available resources to the set of base stations based on theoverall metrics for the plurality of resource partitioning actions. 2.The method of claim 1, wherein the determining allocation of theavailable resources comprises selecting one of the plurality of resourcepartitioning actions based on the overall metrics for the plurality ofresource partitioning actions, and determining allocation of theavailable resources to a base station in the set of base stations basedon a resource usage profile associated with the selected action andapplicable for the base station.
 3. The method of claim 1, wherein theavailable resources are for downlink, and wherein each resource usageprofile comprises a list of transmit power spectral density (PSD) levelsfor the available resources, one transmit PSD level for each availableresource.
 4. The method of claim 1, wherein the available resources arefor uplink, and wherein each resource usage profile comprises a list oftarget interference-over-thermal (loT) levels for the availableresources, one target loT level for each available resource.
 5. Themethod of claim 2, further comprising: scheduling data transmission forat least one user equipment (UE) on the available resources based on theresource usage profile for the base station.
 6. The method of claim 1,wherein a local metric for a resource partitioning action is indicativeof performance achieved by a base station for the resource partitioningaction, and wherein an overall metric for a resource partitioning actionis indicative of overall performance achieved by the set of basestations for the resource partitioning action.
 7. The method of claim 1,further comprising: sending the computed local metrics to the at leastone neighbor base station to enable the at least one neighbor basestation to compute the overall metrics for the plurality of resourcepartitioning actions.
 8. The method of claim 1, wherein the obtainingthe overall metrics comprises, for each resource partitioning action,combining a local metric computed by the base station for the resourcepartitioning action with at least one local metric received from the atleast one neighbor base station for the resource partitioning action toobtain an overall metric for the resource partitioning action.
 9. Themethod of claim 1, wherein each resource partitioning action isassociated with a set of resource usage profiles for the set of basestations, and wherein the computing the local metrics comprises, foreach resource partitioning action, determining at least one rate for atleast one user equipment (UE) communicating with the base station basedon the set of resource usage profiles associated with the resourcepartitioning action, and determining a local metric for the resourcepartitioning action based on the at least one rate.
 10. The method ofclaim 9, wherein the determining the at least one rate comprisesdetermining the at least one rate for the at least one UE based furtheron channel gains between each UE and the set of base stations.
 11. Themethod of claim 1, wherein the local metrics for the plurality ofresource partitioning actions are computed based on a function of rate,or latency, or queue size, or a combination thereof.
 12. The method ofclaim 1, wherein the local metrics for the plurality of resourcepartitioning actions are computed based on a function of sum of rates,or minimum of rates, or sum of quantities determined based on rates. 13.The method of claim 1, wherein each of the plurality of resourcepartitioning actions modifies only one of the available resources. 14.The method of claim 1, wherein each base station in the set of basestations is associated with a list of transmit power spectral density(PSD) levels for the available resources, and wherein each resourcepartitioning action changes transmit PSD by at most one level for anybase station in the set of base stations.
 15. The method of claim 1,wherein a set of action types is supported, and wherein each of theplurality of resource partitioning actions is of one of the set ofaction types.
 16. The method of claim 15, wherein the plurality ofresource partitioning actions comprise first resource partitioningactions for a base station increasing its transmit power spectraldensity (PSD), or second resource partitioning actions for the basestation decreasing its transmit PSD, or third resource partitioningactions for one or more neighbor base stations increasing their transmitPSD, or fourth resource partitioning actions for the one or moreneighbor base stations decreasing their transmit PSD, or fifth resourcepartitioning actions for the base station increasing its transmit PSDand the one or more neighbor base stations decreasing their transmitPSD, or sixth resource partitioning actions for the base stationdecreasing its transmit PSD and the one or more neighbor base stationsincreasing their transmit PSD, or a combination thereof.
 17. The methodof claim 9, wherein each of the at least one UE is associated with anactive set of base stations having received signal quality or receivedsignal strength above a threshold.
 18. The method of claim 1, whereinthe set of base stations includes base stations in active sets of UEscommunicating with the base station, or base stations serving UEs havingactive sets that include the base station, or both.
 19. The method ofclaim 1, wherein a first subset of the computed local metrics and afirst subset of the received local metrics are exchanged between thebase station and the at least one neighbor base station periodically.20. The method of claim 19, wherein a second subset of the computedlocal metrics and a second subset of the received local metrics areexchanged between the base station and the at least one neighbor basestation when requested.
 21. The method of claim 1, wherein the set ofbase stations includes a first base station having a first maximumtransmit power level and a second base station having a second maximumtransmit power level that is different from the first maximum transmitpower level.
 22. An apparatus for wireless communication, comprising:means for determining a plurality of resource partitioning actionsrelated to allocation of available resources among a set of basestations, each resource partitioning action being associated with a setof resource usage profiles for the set of base stations, one resourceusage profile for each base station, each resource usage profileindicating allowed usage of the available resources by a particular basestation; means for computing local metrics for the plurality of resourcepartitioning actions by a base station of the set of base stations;means for receiving, by the base station, local metrics for theplurality of resource partitioning actions from at least one neighborbase station in the set of base stations; means for obtaining overallmetrics for the plurality of resource partitioning actions, the overallmetrics based at least in part on the computed local metrics and thereceived local metrics for the plurality of resource partitioningactions; and means for determining allocation of the available resourcesto the set of base stations based on the overall metrics for theplurality of resource partitioning actions.
 23. The apparatus of claim22, wherein each resource partitioning action is associated with a setof resource usage profiles for the set of base stations, and wherein foreach resource partitioning action the means for computing the localmetrics determines at least one rate for at least one user equipment(UE) communicating with the base station based on the set of resourceusage profiles associated with the resource partitioning action, anddetermines a local metric for the resource partitioning action based onthe at least one rate.
 24. The apparatus of claim 22, wherein the meansfor determining allocation of the available resources selects one of theplurality of resource partitioning actions based on the overall metricsfor the plurality of resource partitioning actions, and determines anallocation of the available resources to a base station in the set ofbase stations based on a resource usage profile associated with theselected action and applicable for the base station.
 25. An apparatusfor wireless communication, comprising: at least one processorconfigured to: determine a plurality of resource partitioning actionsrelated to allocation of available resources among a set of basestations, each resource partitioning action being associated with a setof resource usage profiles for the set of base stations, one resourceusage profile for each base station, each resource usage profileindicating allowed usage of the available resources by a particular basestation; compute local metrics for the plurality of resourcepartitioning actions by a base station of the set of base stations;receive, by the base station, local metrics for the plurality ofresource partitioning actions from at least one neighbor base station inthe set of base stations; obtain overall metrics for the plurality ofresource partitioning actions, the overall metrics based at least inpart on the computed local metrics and the received local metrics forthe plurality of resource partitioning actions; and determine allocationof the available resources to the set of base stations based on theoverall metrics for the plurality of resource partitioning actions. 26.A computer program product, comprising a non-transitorycomputer-readable medium comprising: code for causing at least onecomputer to determine a plurality of resource partitioning actionsrelated to allocation of available resources among a set of basestations, each resource partitioning action being associated with a setof resource usage profiles for the set of base stations, one resourceusage profile for each base station, each resource usage profileindicating allowed usage of the available resources by a particular basestation; code for causing the at least one computer to computing localmetrics for the plurality of resource partitioning actions by a basestation of the set of base stations; code for causing the at least onecomputer to receiving, by the base station, local metrics for theplurality of resource partitioning actions from at least one neighborbase station in the set of base stations; code for causing the at leastone computer to obtain overall metrics for the plurality of resourcepartitioning actions, the overall metrics based at least in part on thecomputed local metrics and the received local metrics for the pluralityof resource partitioning actions, and code for causing the at least onecomputer to determine allocation of the available resources to the setof base stations based on the overall metrics for the plurality ofresource partitioning actions.